Stock Solution Automation and Nutrient Replenishment Workflow

An autonomous agent ingests fertigation event logs from the climate computer (e.g., Priva, Argus) and real-time weight data from load cells under stock tanks. It calculates the remaining volume of each A/B/C stock solution and forecasts depletion timelines based on the crop irrigation schedule. This eliminates guesswork and prevents run-dry events that can halt production.

Upon reaching a replenishment threshold, the workflow triggers a multi-step mixing sequence. It sends precise weight targets for raw fertilizer salts (e.g., calcium nitrate, potassium sulfate) to automated scales, controls water solenoids for volume, and orchestrates agitators and pH/EC probes. This is integrated with dosing equipment (e.g., Dosatron, Injectomate) and raw material inventory systems to deduct used stock.

Before the new batch is pumped to the main stock tank, inline sensors verify pH, EC, and temperature against recipe specifications. An AI validation agent compares readings to expected values and historical trends. Any off-spec solution is automatically diverted to a quarantine tank, and alerts are routed to the grower's dashboard and mobile devices, preventing contaminated solution from entering the crop loop.

The workflow creates a two-way sync with enterprise systems (e.g., SAP S/4HANA, Oracle Netsuite, or a farm-specific ERP). Each mixing event automatically updates raw material inventory levels. When salt bins fall below a reorder point, the system can generate purchase requisitions or trigger supplier APIs. This closes the loop between consumption, replenishment, and procurement, optimizing working capital.

The architecture includes defined escalation paths for anomalies: sensor drift, failed validation, or inventory discrepancies. These exceptions are routed to a human review queue within a platform like ServiceNow or a custom dashboard. Approvals for overrides (e.g., using a substitute salt) are logged with rationale, maintaining an audit trail for food safety (GFSI) and operational governance.

Every action—from forecast to validation—is logged with timestamps and data snapshots to an observability platform (e.g., Datadog, Grafana). Pre-built reports show solution cost per liter, mixing efficiency, and input variance. This data feeds back into the forecasting agent for continuous improvement, and supports sustainability reporting by tracking exact nutrient use per crop batch.

This workflow directly targets labor-intensive, error-prone manual stock tank management. By automating monitoring, calculation, and mixing, you eliminate daily manual checks and complex batching math. The primary savings come from a 70-80% reduction in labor hours dedicated to solution preparation and a 15-25% reduction in fertilizer waste from over-concentration or incorrect blends. Secondary benefits include improved crop consistency by eliminating human mixing errors and preventing costly irrigation system damage from off-spec solutions.

The architecture is a multi-agent system orchestrated by a central engine (e.g., LangGraph). Key components include:

• Ingestion Agents: Pull real-time data from load cells (scale systems), water meters, and fertigation controller logs.
• Calculation Engine: Models nutrient depletion based on irrigation volume and pre-set solution recipes.
• Dosing Controller: Interfaces with PLCs to activate raw salt hoppers, water valves, and mixing pumps.
• Validation Layer: Uses inline EC/pH sensors to test the new batch before release, with logic to recirculate or alert.
• Inventory Manager: Tracks raw salt consumption and triggers reorder alerts integrated with your ERP or procurement system.

Reliable automation depends on clean, accessible data from legacy systems. Critical integrations include:

• Scale/PLC Systems: API or Modbus connectivity to tank weight sensors.
• Fertigation Controllers (e.g., Priva, Argus): To log irrigation events and solution usage.
• Inventory Management: Connection to track raw material SKUs and lot numbers.
• Alerting Platforms (e.g., PagerDuty, Teams): For operational notifications.

The largest implementation risk is poor sensor calibration or missing irrigation data, which corrupts depletion models. A phased rollout should start with monitoring and alerting before enabling fully autonomous mixing.

Full autonomy requires guardrails. The workflow must include:

• Pre-Mix Approval Gates: For batches exceeding a defined volume or cost threshold, requiring a grower's sign-off via a mobile dashboard.
• Off-Spec Routing: If the validation layer fails, the batch is quarantined, and an alert is routed to a maintenance technician with diagnostic data (sensor readings, recipe deviation).
• Human-in-the-Loop Override: Manual override capabilities at the dosing panel and within the orchestration UI for emergency stops or manual recipe input.
• Audit Trail: Every action—calculation, command, validation result—is logged with a timestamp and user/agent ID for full traceability and compliance.

De-risk the build with a phased approach:

1. Phase 1 – Monitoring & Alerts (2-3 weeks): Deploy agents to ingest scale and irrigation data. Build dashboards showing calculated depletion and send "Low Stock" alerts. No control actions.
2. Phase 2 – Assisted Mixing (3-4 weeks): Introduce the dosing controller. The system generates a precise mixing recipe and displays step-by-step instructions for a human to execute, validating the mechanical integration.
3. Phase 3 – Closed-Loop Automation (2 weeks): Enable full autonomy for a single, non-critical stock tank. Incorporate the validation layer and exception routing. Monitor closely for one full crop cycle.
4. Phase 4 – Scale-Out: Roll out to remaining tanks and integrate with inventory management.

Post-deployment, the system requires monitoring for drift and optimization opportunities.

• Performance Dashboard: Track key metrics: batch accuracy (EC/pH vs. target), raw material consumption vs. forecast, and mean time to respond to off-spec events.
• Model Retraining: The depletion calculation model should be periodically retrained on actual crop uptake data versus irrigation volume to improve accuracy over seasons.
• Alert Tuning: Refine alert thresholds to reduce noise while ensuring critical issues are caught. Integrate with on-call schedules for the maintenance team.
• Change Management: Maintain a digital twin or simulation environment to test new crop recipes or equipment changes before they hit the live system.